EkoH

EkoH (Reputation & Expertise) — first sub‑module under Kollective Intelligence.
Implements seven core services with clear code‑names, supported by dedicated models and fixed parameters.

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1) Functional Services (and expected files)

Code‑name list per the v14 inventory; each code‑name maps to a Django service module (e.g., services/scoring.py contains multidimensional_scoring).

Display name	Code name / service	Purpose / behavior	Likely file or module
Multidimensional Scoring	multidimensional_scoring	Compute per‑user/content scores across axes (quality, frequency, relevance, expertise).	services/scoring.py
Criteria Customization	configuration_weights	Adjust scoring weights per axis/domain; read from stored configuration.	services/configuration.py (reads ScoreConfiguration)
Automatic Contextual Analysis	contextual_analysis	AI tweaks sub‑scores in real time by topic/history/complexity signals.	services/contextual_analysis.py
Dynamic Privacy	privacy_settings	Enforce anonymity/pseudonym modes while still exposing merit outputs.	services/privacy.py
History & Traceability	score_history	Persist every recalculation/config change for auditability.	services/history.py (+ model hooks)
Interactive Visualizations	score_visualization	Serve aggregated data for dashboards/skill maps/matrices.	services/visualization.py
Expertise Classification by Field	expertise_field_classification	Bind scores to formal knowledge domains (taxonomy).	services/expertise.py

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2) Backend Functionalities

• Reputation engine & triggers. A Django service updates users’ domain‑specific Ekoh scores from platform activity; scheduled Celery jobs perform periodic recalculation, and event hooks apply immediate updates on impactful actions.

• Ethical multiplier. An ethics score multiplies domain expertise to produce final influence weights (raises for constructive behavior, lowers for flagged behavior).

• Smart‑Vote integration. Voting across modules (e.g., Ethikos) is weighted by the voter’s relevant Ekoh score; live results may be pushed via Channels.

• Cross‑module APIs. Provides shared search/notifications/feed/recommendation surfaces that consume Ekoh signals (e.g., leaderboards, relevance).

• Quality controls. Thresholds and moderation safeguards prevent brigading/spam from distorting reputation and consensus.

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3) Database Models (OLTP)

Canonical tables powering EkoH scoring, ethics, audit, and privacy.

Table / Model	Purpose	Key fields
ExpertiseCategory	Domain taxonomy for expertise classification.	id, name
UserExpertiseScore	Per‑user per‑domain raw/weighted score.	id, user, category, raw_score, weighted_score
UserEthicsScore	Per‑user ethical multiplier (applied to expertise).	user (PK), ethical_score
ScoreConfiguration	Named weights/coefficients (global or per field).	id, weight_name, weight_value, field
ContextAnalysisLog	AI context adjustments applied to scores.	id, entity_type, entity_id, field, input_metadata (JSON), adjustments_applied (JSON)
ConfidentialitySetting	User privacy level for identity display near scores.	user (PK), level (enum: public/pseudonym/anonymous)
ScoreHistory	Full audit trail of score changes.	id, merit_score (FK), old_value, new_value, change_reason

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4) Supporting Configuration (frozen)

Finalized parameters for EkoH engine and domain taxonomy.

• Initial axis weights: quality=1.000, expertise=1.500, frequency=0.750 → used by multidimensional_scoring.

• Ethical multiplier bounds: floor 0.20, cap 1.50.

• Expertise domains: EXPERTISE_DOMAIN_CHOICES (26 ISO‑based domains; seeded fixtures).

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5) Schedules & runtime

• Periodic recomputation: Celery Beat tasks (nightly/interval) to refresh Ekoh scores and any precomputed leaderboards; monitored in CI/ops.

• Realtime delivery: Optionally push score/leaderboard deltas or weighted results via Django Channels + Redis.

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Summary

EkoH exposes seven concrete services (multidimensional_scoring, configuration_weights, contextual_analysis, privacy_settings, score_history, score_visualization, expertise_field_classification) mapped to Django service modules; it persists expertise/ethics/traceability/privacy via dedicated tables and operates under fixed, reviewable parameters. It is the weighting backbone for Smart‑Vote and cross‑module relevance, with periodic recomputation and optional realtime updates.